## What is the Difference Between Kurtosis and Skewness?

- Categorized in: Six Sigma Implementation, Six Sigma Tools & Metrics

A Six Sigma review of any operation or process will involve the analysis of large sets of data to come to sound decisions. It is a well-established business method that has been used for the past 20 years to save companies millions of dollars and make operations much more efficient.

The goal in Six Sigma is to be able to run a nearly flawless operation. There should be no variance whatsoever in the function that is being performed. Whether it is a manufacturing line or a call center, the goal is to be able to complete the task in an error-free way every time. When a data sample is charted and there are big variations in the numbers, that can signal a problem. A chart with big peaks is called kurtosis. The word comes from a Greek word which means bulging.

Analyzing the data that is collected is the job of Six Sigma Black Belts who lead the reviews and use the charts and graphs produced to identify flaws that need to be corrected. Kurtosis and skewness are two of the distributions that the black belt will look for to highlight where there is too much variance in the process.

In a perfect process, there would be negative kurtosis because the graph would be almost a flat line. When there is positive kurtosis however, you have a huge swing in data values that can be an indication of a problem. If the sample size is large enough to be a true reflection on the operation, it is imperative to figure out why there is such huge variance. If you are dealing with a small sample size, do not read too much into kurtosis.

Skewness is another statistical term that can indicate too much variance. Like kurtosis, the values are unevenly spread out on a graph. Skewness measures the asymmetry of the distribution. A true symmetrical distribution would put an equal number of values on either side of the mean. When too many values fall to the left, you have negative symmetry, and when more numbers go to the right of the mean, you have positive symmetry.

When numbers are skewed in either direction, the Six Sigma black belt knows that this could be a problem. The goal is to reduce variance, and any skewness that is shown means that the process is failing to produce the same results over and over again.